where \(\Theta \) =\(\lbrace \)\(A\)\(,W_t\rbrace \) . \(\eta \)  denotes the learning rate, which is parameter-dependent if some auto-adaptive SGD models are applied. In our proposed model, we set Adagrad [1]} as our SGD method. We can 1) sample the labeled professional malicious users repeatedly to build more samples (for each labeled professional malicious user, select 5 to 10 times unlabeled normal users to learn the metric); 2) enhance the importance of labeled users (a large \(\lambda \) ). To validate the performance, we can monitor the return on a holdout validation dataset (which is the original data in Fig. REF ). In this way, we can achieve a learned metric matrix \(A_\text{L}\) .
